Learning Ontologies for the Semantic Web
(2001)

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on di erent types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, re nement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, TextTo-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. 1. ONTOLOGIES FOR THE SEMANTIC WEB Conceptual structures that de ne an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly de ned and machine processable semantics. By de ning shared and common domain theories, ontologies help both people and machines to communicate concisely, supporting the exchange of semantics and not only syntax. Hence, the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission by the authors. Semantic Web Workshop 2001 Hongkong, China Copyright by the authors. cheap and fast construction of domain-speci c ontologies is crucial for the success and the proliferation of the Semantic Web. Though ontology engineering tools have become mature over the last decade (cf. [9]), the manual acquisition of ontologies still remains a tedious, cumbersome task resulting easily in a knowledge acquisition bottleneck. Having developed our ontology engineering workbench, OntoEdit, we had to face exactly this issue, in particular we were given questions like Can you develop an ontology fast? (time) Is it diÆcult to build an ontology? (diÆculty) How do you know that you've got the ontology right? (con dence) In fact, these problems on time, diÆculty and con dence that we ended up with were similar to what knowledge engineers had dealt with over the last two decades when they elaborated on methodologies for knowledge acquisition or workbenches for de ning knowledge bases. A method that proved extremely bene cial for the knowledge acquisition task was the integration of knowledge acquisition with machine learning techniques [33]. The drawback of these approaches, e.g. the work described in [21], however, was their rather strong focus on structured knowledge or data bases, from which they induced their rules. In contrast, in the Web environment that we encounter when building Web ontologies, the structured knowledge or data base is rather the exception than the norm. Hence, intelligent means for an ontology engineer takes on a di erent meaning than the | very seminal | integration architectures for more conventional knowledge acquisition [7]. Our notion of Ontology Learning aims at the integration of a multitude of disciplines in order to facilitate the construction of ontologies, in particular machine learning. Because the fully automatic acquisition of knowledge by machines remains in the distant future, we consider the process of ontology learning as semi-automatic with human intervention, adopting the paradigm of balanced cooperative modeling [20] for the construction of ontologies for the Semantic Web. This objective in mind, we have built an architecture that combines knowledge acquisition with machine learning, feeding on the resources that we nowadays nd on the syntactic Web, viz. free text, semi-structured text, schema de nitions (DTDs), etc. Thereby, modules in our framework serve different steps in the engineering cycle, which here consists of the following ve steps (cf. Figure 1): First, existing ontologies are imported and reused by merging existing structures or de ning mapping rules between existing structures and the ontology to be established. For instance, [26] describe how ontological structures contained in Cyc are used in order to facilitate the construction of a domain-speci c ontology. Second, in the ontology extraction phase major parts of the target ontology are modeled with learning support feeding from web documents. Third, this rough outline of the target ontology needs to be pruned in order to better adjust the ontology to its prime purpose. Fourth, ontology re nement pro ts from the given domain ontology, but completes the ontology at a ne granularity (also in contrast to extraction). Fifth, the prime target application serves as a measure for validating the resulting ontology [31]. Finally, one may revolve again in this cycle, e.g. for including new domains into the constructed ontology or for maintaining and updating its scope. 2. THE ONTOLOGY LEARNING KILLER APPLICATION Though ontologies and their underlying data in the Semantic Web are envisioned to be reusable for a wide range of possibly unforeseen applications, a particular target application remains the touchstone for a given ontology. In our case, we have been dealing with ontology-based knowledge portals that structure Web content and that allow for structured provisioning and accessing of data [29, 30]. Knowledge portals are information intermediaries for knowledge accessing and sharing on the Web. The development of a knowledge portal consists of the tasks of structuring the knowledge, establishing means for providing new knowledge and accessing the knowledge contained in the portal. A considerable part of development and maintenance of the portal lies in integrating legacy information as well as in constructing and maintaining the ontology in vast, often unknown, terrain. For instance, a knowledge portal may focus on the electronics sector, integrating comparative shopping in conjunction with manuals, reports and opinions about current electronic products. The creation of the background ontology for this knowledge portal involves tremendous e orts for engineering the conceptual structures that underly existing warehouse databases, product catalogues, user manuals, test reports and newsgroup discussions. Correspondingly, ontology structures must be constructed from database schemata, a given product thesaurus (like BMEcat), XML documents and document type de nitions (DTDs), and free texts. Still worse, signi cant parts of these (meta-)data change extremely fast and, hence, require a regular update of the corresponding ontology parts. Thus, very di erent types of (meta-)data might be useful input for the construction of the ontology. However, in practice one needs comprehensive support in order to deal Just like Tim Berners-Lee did not forsee online auctions being a common business model of the Web of 2000. Comprehensive support with ontology learning need not necessarily imply the top-notch learning algorithms, but may rely more heavily on appropriate tool support and methodology. with this wealth. Hence, there comes the need for a range of di erent techniques: Structured data and meta data require reverse engineering approaches, free text may contribute to ontology learning directly or through information extraction approaches. Semi-structured data may nally require and pro t from both. In the following we elaborate on our ontology learning framework. Thereby we approach di erent techniques for di erent types of data, showing parts of our architecture, its current status, and parts that may complement our current Text-To-Onto environment. A general overview of ontology learning techniques as well as corresponding references may be found in Section 9. 3. AN ARCHITECTURE FOR ONTOLOGY LEARNING Given the task of constructing and maintaining an ontology for a Semantic Web application, e.g. for an ontologybased knowledge portal that we have been dealing with (cf. [29]), we have produced a wish list of what kind of support we would fancy. 3.1 Ontology Engineering WorkbenchOntoEdit As core to our approach we have built a graphical user interface to support the ontology engineering process manually performed by the ontology engineer. Here, we o er sophisticated graphical means for manual modeling and re ning the nal ontology. Di erent views are o ered to the user targeting the epistemological level rather than a particular representation language. However, the ontological structures built there may be exported to standard Semantic Web representation languages, such as OIL and DAML-ONT, as well as our own F-Logic based extensions of RDF(S). In addition, executable representations for constraint checking and application debugging can be generated and then accessed via SilRi, our F-Logic inference engine, that is directly connected with OntoEdit. The sophisticated ontology engineering tools we knew, e.g. the Prot eg e modeling environment for knowledge-based systems [9], would o er capabilities roughly comparable to OntoEdit. However, given the task of constructing a knowledge portal, we f